The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Autor: Allison B. Marquardt Collow, Jonathan J. Rutz, Gary A. Wick, Christine A. Shields, Karthik Kashinath, Anna Wilson, Alexandre M. Ramos, Michael Wehner, Tamara Shulgina, Harinarayan Krishnan, Naomi Goldenson, Scott Sellars, Elizabeth McClenny, Swen Brands, Daniel Walton, Maximiliano Viale, Ashley E. Payne, Prabhat, Vitaliy Kurlin, Irina Gorodetskaya, Grzegorz Muszynski, Travis A. O'Brien, Helen Griffith, David A. Lavers, Duane E. Waliser, Gudrun Magnusdottir, Paul A. Ullrich, Kelly Mahoney, Chandan Sarangi, Ricardo Tomé, Bin Guan, Juan M. Lora, Brian Kawzenuk, Phu Nguyen, Yun Qian, F. Martin Ralph, L. Ruby Leung
Rok vydání: 2019
Předmět:
Zdroj: Journal of Geophysical Research: Atmospheres
ISSN: 2169-8996
2169-897X
Popis: Author(s): Rutz, JJ; Shields, CA; Lora, JM; Payne, AE; Guan, B; Ullrich, P; O’Brien, T; Leung, LR; Ralph, FM; Wehner, M; Brands, S; Collow, A; Goldenson, N; Gorodetskaya, I; Griffith, H; Kashinath, K; Kawzenuk, B; Krishnan, H; Kurlin, V; Lavers, D; Magnusdottir, G; Mahoney, K; McClenny, E; Muszynski, G; Nguyen, PD; Prabhat, M; Qian, Y; Ramos, AM; Sarangi, C; Sellars, S; Shulgina, T; Tome, R; Waliser, D; Walton, D; Wick, G; Wilson, AM; Viale, M | Abstract: Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.
Databáze: OpenAIRE
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